Lisa Eun

Lisa Eun

Unifying Clinical Trial Operations into a Single Workspace

Unifying Clinical Trial Operations into a Single Workspace

Designing a centralized system that connects fragmented clinical trial workflows—enabling real-time visibility, streamlined execution, and reduced operational overhead

[Industry]

Healthcare | SaaS

[My Role]

Lead Product Designer

[Platform]

Desktop

Accelerating Clinical Trial Performance

Accelerating Clinical Trial Performance

Accelerating Clinical Trial Performance

This work focused on improving visibility across study operations, monitoring workflows, milestones, and data management through a centralized product ecosystem.
This work focused on improving visibility across study operations, monitoring workflows, milestones, and data management through a centralized product ecosystem.
Pi Health publicly positioned the platform around faster trials and improved data quality. My work supported that broader product vision through unified operational workflows.
Pi Health publicly positioned the platform around faster trials and improved data quality. My work supported that broader product vision through unified operational workflows.

Problem

Journey

Solution

Fragmented Clinical Operations

Clinical trial teams rely on multiple disconnected tools, duplicate entries, and slow reconciliation across systems

EDC

CTMS

Spreadsheets / Email

Delayed Visibility

Critical data is scattered, making real-time oversight difficult

Manual Workarounds

Teams spent hours on exports, reconciliations, and duplicate data entry

Operational Risk

Higher chance of error and compliance gaps across studies and sites

Problem

Journey

Solution

(01)

Fragmented Clinical Operations

Spreadsheets / Email

CTMS

EDC

Clinical trial teams rely on multiple disconnected tools, duplicate entries, and slow reconciliation across systems

Delayed Visibility

Critical data is scattered, making real-time oversight difficult

Manual Workarounds

Teams spent hours on exports, reconciliations, and duplicate data entry

Operational Risk

Higher chance of error and compliance gaps across studies and sites

A Look into Key Screens

Disclaimer:

Visuals in this case study have been reconstructed with fictional data to protect confidentiality. They reflect real design challenges, workflows, and contributions.

01

Study Overview

A centralized command center that provides a real-time snapshot of study health and progress

Unified view of study life cycle and performance

At-a-glance indicator health indicators for faster decisions

Brings together key data across multiple workflows, reducing tool-switching and improving visibility

02

Milestone Tracker

A shared execution layer for managing study timelines, ownership, dependencies, and critical deadlines

Replaced scattered trackers with one place to manage milestone status, planned dates, and owners

Highlighted overdue or slipping milestones early so teams could act before downstream impact

Supported startup, regulatory, enrollment, and closeout milestones in one shared workflow

03

Monitoring Visits

Structured workflows that help CRAs plan, execute, and document visits with greater consistency

Guided workflows for visit execution

Task-level tracking and completion

Streamlined documentation and reporting tasks that are often repetitive or manual

04

Data Management Dashboard

A centralized workspace that consolidates data entered across multiple workflows and user roles

Brought together inputs from visits, forms, queries, monitoring activity, and operational workflows into

Supported inputs from both site and sponsor users within a unified workspace, while preserving appropriate blinding and permission controls

Surfaced trends, missing data, and quality risks earlier through centralized monitoring

Minimized time spent comparing spreadsheets, exports, and disconnected systems

05

Query Management

Unifying workload tracking, prioritizing, and resolving data queries across the study

Clear ownership and form context

Faster resolution life cycle

Prioritized by severity and status

Searchable, navigational records that stem from different areas of the application

How It Works?

My Design Process

(01)

Map & Understand

Explored how different teams worked the tools they used, and the challenges caused by fragmented systems

  • Map end-to-end workflows of the study lifecycle

  • Understand different perspectives of stakeholders across sites, monitoring, data management, Clinops

  • Identify pain points, gaps, and opportunities to unify

(02)

Define & Prioritize

Synthesized requirements and data to define what matters most and create a shared product direction

  • Consolidated and prioritized requirements across teams

  • Defined key data points, user needs, and success metrics

  • Aligned on MVP scope and phased roadmap

(03)

Prototyping & Testing

Creating interactive prototypes to test ideas and refine usability through real feedback.

  • Consolidated and prioritized requirements across teams

  • Defined key data points, user needs, and success metrics

  • Aligned on MVP scope and phased roadmap

(03)

Design for Execution

Designed intuitive experiences that surface key signals, simplify workflows, and help teams act with confidence

  • Simplified multi-step workflows into clear, guided flows

  • Reduced context switching across tools and pages

  • Designed dashboards to surface key signals and priorities

(02)

Concept Development

Shaping ideas into concepts, structuring flows and features around real user goals.

  • Simplified multi-step workflows into clear, guided flows

  • Reduced context switching across tools and pages

  • Designed dashboards to surface key signals and priorities

(04)

Validate & Build for Scale

Validated with key stakeholders, iterated quickly, and built scalable foundations to support future growth

  • Tested with stakeholders and incorporated feedback

  • Iterated on usability, edge cases, and performance

  • Built reusable components and flexible layouts for scale

Tools Behind the Work

  • FigJam

  • Figma

  • Chat GPT

  • Figma Make

  • Cursor AI

  • Miro

  • Adobe Creative Suite

  • Atlassian

Disclaimer:
Visuals in this case study have been reconstructed with fictional data to protect confidentiality. They reflect real design challenges, workflows, and contributions.
Disclaimer:
Visuals in this case study have been reconstructed with fictional data to protect confidentiality. They reflect real design challenges, workflows, and contributions.

Problem

Journey

Solution

Fragmented Clinical Operations

Clinical trial teams rely on multiple disconnected tools, duplicate entries, and slow reconciliation across systems

EDC

CTMS

Spreadsheets / Email

Delayed Visibility

Critical data is scattered, making real-time oversight difficult

Manual Workarounds

Teams spent hours on exports, reconciliations, and duplicate data entry

Operational Risk

Higher chance of error and compliance gaps across studies and sites

Impact

By consolidating fragmented workflows into a unified platform experience, the product helped teams move through complex trial operations with greater visibility, consistency, and confidence.

- Unified operational workflows across studies, sites, and data management activities

- Supported a platform associated with 61% faster trial execution across 8 countries

- Built scalable UX patterns for future workflows and modules

- Improved operational efficiency, including feasibility timelines reduced by ~1 month and site responses occurring 37 days earlier on average

Reflection

Designing enterprise software in regulated environments taught me that the challenge is rarely the interface itself. It’s making complexity understandable.

This project pushed me to think beyond individual screens and focus on how workflows, stakeholders, permissions, and operational data connect across an ecosystem.

I learned how to:

- balance multiple stakeholder needs

- simplify high-complexity workflows

- design for scalability and governance

- create systems that support both usability and operational trust